Paris Airbnb - visualisation data project

Author: Wiktoria Ekwińska

The open-access dataset on which the analysis was conducted comes from Airbnb platform and it was downloaded via this website

It contains the data, collected from the above mentioned platform, relating to residential homes available for short-term rental from private persons in Paris.

Goals

From the analysis we want to learn the following:

• in which neighbourhoods the prices per night are the lowest and where are the highest
• what type of rented property is the cheapest and the most expensive
• are any of the variables correlated
• is there a relationship between the number of reviews (of the property) and its price
• is there a relationship between the availability (of the property) and its price
• what offers are the most popular (=have the highest number of reviews), where they are located and what their availability looks like
• what is the layout of all the listings on the map of Paris, with price and availability demonstration
• what is the layout of the most popular offers on the map of Paris, are they located near tourist attractions
• where are located the most booked offers (= with the lowest availability)

About Paris Airbnb data

Variables:

• id - unique Airbnb identifier
• name - name
• host_id - host's identifier
• host_name - host's name
• neighbourhood_group - the neighbourhood group as geocoded using the latitude and longitude against neighborhoods as defined by open or public digital shapefiles.
• neighbourhood - the neighbourhood as geocoded using the latitude and longitude against neighborhoods as defined by open or public digital shapefiles
• latitude
• longitude
• room_type - the type of rental property
• price - daily price in local currency (here euro)
• minimum_nights - minimum number of night stay for the listing
• number_of_reviews - the number of reviews the listing has
• last_review - the date of the last/newest review
• reviews_per_month - the average number of reviews of the listing
• calculated_host_listing_count - the number of listings the host has in the current scrape, in the city/region geography.
• availability_365 - the availability of the listing x days in the future as determined by the calendar
• number_of_reviews_ltm - The number of reviews the listing has (in the last 12 months)
• licence

Source

import numpy as np
import pandas as pd
import seaborn as sns
from matplotlib import pyplot as plt
import plotly.express as px
import ipywidgets as widgets
from ipywidgets import interact, interact_manual

import geopandas as gpd
import math

import folium
from folium import Choropleth, Circle, Marker
from folium.plugins import HeatMap, MarkerCluster

Uploading the data

airbnb = pd.read_csv('listings.csv')

airbnb.head(5)

	id	name	host_id	host_name	neighbourhood_group	neighbourhood	latitude	longitude	room_type	price	minimum_nights	number_of_reviews	last_review	reviews_per_month	calculated_host_listings_count	availability_365	number_of_reviews_ltm	license
0	5396	Explore the heart of old Paris	7903	Borzou	NaN	Hôtel-de-Ville	48.85207	2.35871	Entire home/apt	100	2	263	2020-08-08	2.68	1	55	35	7510402838018
1	7397	MARAIS - 2ROOMS APT - 2/4 PEOPLE	2626	Franck	NaN	Hôtel-de-Ville	48.85909	2.35315	Entire home/apt	105	10	282	2021-09-29	2.28	1	233	13	7510400829623
2	7964	Large & sunny flat with balcony !	22155	Anaïs	NaN	Opéra	48.87417	2.34245	Entire home/apt	130	6	6	2015-09-14	0.07	1	293	0	7510903576564
3	9359	Cozy, Central Paris: WALK or VELIB EVERYWHERE !	28422	Bernadette	NaN	Louvre	48.85899	2.34735	Entire home/apt	75	180	0	NaN	NaN	1	58	0	Available with a mobility lease only ("bail mo...
4	9952	Paris petit coin douillet	33534	Elisabeth	NaN	Popincourt	48.86227	2.37134	Entire home/apt	80	4	32	2021-06-23	0.51	1	212	7	7511101582862

airbnb.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 49634 entries, 0 to 49633
Data columns (total 18 columns):
 #   Column                          Non-Null Count  Dtype  
---  ------                          --------------  -----  
 0   id                              49634 non-null  int64  
 1   name                            49587 non-null  object 
 2   host_id                         49634 non-null  int64  
 3   host_name                       49606 non-null  object 
 4   neighbourhood_group             0 non-null      float64
 5   neighbourhood                   49634 non-null  object 
 6   latitude                        49634 non-null  float64
 7   longitude                       49634 non-null  float64
 8   room_type                       49634 non-null  object 
 9   price                           49634 non-null  int64  
 10  minimum_nights                  49634 non-null  int64  
 11  number_of_reviews               49634 non-null  int64  
 12  last_review                     38625 non-null  object 
 13  reviews_per_month               38625 non-null  float64
 14  calculated_host_listings_count  49634 non-null  int64  
 15  availability_365                49634 non-null  int64  
 16  number_of_reviews_ltm           49634 non-null  int64  
 17  license                         27933 non-null  object 
dtypes: float64(4), int64(8), object(6)
memory usage: 6.8+ MB

The dataset contains 18 columns and 49634 rows. It should be noted that for some of the variables there is missing data. We're going to get more information about the missing data.

airbnb.isnull().sum()

id                                    0
name                                 47
host_id                               0
host_name                            28
neighbourhood_group               49634
neighbourhood                         0
latitude                              0
longitude                             0
room_type                             0
price                                 0
minimum_nights                        0
number_of_reviews                     0
last_review                       11009
reviews_per_month                 11009
calculated_host_listings_count        0
availability_365                      0
number_of_reviews_ltm                 0
license                           21701
dtype: int64

Missing data appears in case of 6 columns. As it turns out, the column 'neighbourhood_group' doesn't contain any values so it's going to be removed. Moreover, other columns that were dropped are 'id', 'host_name' and 'license'.

airbnb = airbnb.drop(['id','host_name','neighbourhood_group','license','number_of_reviews_ltm'], axis=1)
airbnb.head()

	name	host_id	neighbourhood	latitude	longitude	room_type	price	minimum_nights	number_of_reviews	last_review	reviews_per_month	calculated_host_listings_count	availability_365
0	Explore the heart of old Paris	7903	Hôtel-de-Ville	48.85207	2.35871	Entire home/apt	100	2	263	2020-08-08	2.68	1	55
1	MARAIS - 2ROOMS APT - 2/4 PEOPLE	2626	Hôtel-de-Ville	48.85909	2.35315	Entire home/apt	105	10	282	2021-09-29	2.28	1	233
2	Large & sunny flat with balcony !	22155	Opéra	48.87417	2.34245	Entire home/apt	130	6	6	2015-09-14	0.07	1	293
3	Cozy, Central Paris: WALK or VELIB EVERYWHERE !	28422	Louvre	48.85899	2.34735	Entire home/apt	75	180	0	NaN	NaN	1	58
4	Paris petit coin douillet	33534	Popincourt	48.86227	2.37134	Entire home/apt	80	4	32	2021-06-23	0.51	1	212

We're replacing missing data in the column 'reviews_per_month' with the value 0:

airbnb.fillna({'reviews_per_month':0}, inplace = True) 
airbnb.reviews_per_month.isnull().sum(0)

0

Basic statistics

airbnb.describe()

	host_id	latitude	longitude	price	minimum_nights	number_of_reviews	reviews_per_month	calculated_host_listings_count	availability_365
count	4.963400e+04	49634.000000	49634.000000	49634.000000	49634.000000	49634.000000	49634.000000	49634.000000	49634.000000
mean	9.711188e+07	48.863998	2.344889	130.203207	111.961256	21.131080	0.633449	13.192167	100.681569
std	1.179651e+08	0.018146	0.033118	229.383172	169.898643	45.624607	1.148105	47.381927	134.541931
min	2.626000e+03	48.812220	2.221440	0.000000	1.000000	0.000000	0.000000	1.000000	0.000000
25%	1.321955e+07	48.850880	2.324130	60.000000	2.000000	1.000000	0.020000	1.000000	0.000000
50%	3.876027e+07	48.865330	2.347985	90.000000	4.000000	5.000000	0.220000	1.000000	1.000000
75%	1.437275e+08	48.878450	2.369280	140.000000	365.000000	21.000000	0.770000	2.000000	221.000000
max	4.263328e+08	48.905690	2.467120	11600.000000	9999.000000	1711.000000	50.920000	382.000000	365.000000

Firstly, we should notice the high values of standard deviation, which means there is a siginificant dispersion around the mean. Therefore the next step involves visualisation of the distributions of all numeric variables with box plots and density plots (excluding 'host_id', 'latitude' and 'longitude').

Box plots

f, axes = plt.subplots(2, 3, figsize=(15, 10))
sns.boxplot(x=airbnb['price'], palette="flare", ax=axes[0, 0])
sns.boxplot(x=airbnb['minimum_nights'], palette="flare", ax=axes[0, 1])
sns.boxplot(x=airbnb['number_of_reviews'], palette="flare", ax=axes[0, 2])
sns.boxplot(x=airbnb['reviews_per_month'], palette="flare", ax=axes[1, 0])
sns.boxplot(x=airbnb['calculated_host_listings_count'], palette="flare", ax=axes[1, 1])
sns.boxplot(x=airbnb['availability_365'], palette="flare", ax=axes[1, 2])
f.suptitle("Box plots", fontsize = 30)
f.tight_layout()
plt.show()

From the box plots we can see that there are a lot of outliers.

Distribution visualisation using density plots

f, axes = plt.subplots(2, 3, figsize=(15, 10))
sns.histplot(airbnb['price'], color='#D6AA8D', ax=axes[0, 0], kde=True, stat="density", linewidth=0)
sns.histplot(airbnb['minimum_nights'], color='#CC8D7A', ax=axes[0, 1], kde=True, stat="density", linewidth=0)
sns.histplot(airbnb['number_of_reviews'], color='#BB686E', ax=axes[0, 2], kde=True, stat="density", linewidth=0)
sns.histplot(airbnb['reviews_per_month'], color='#AB5A6D', ax=axes[1, 0], kde=True, stat="density", linewidth=0)
sns.histplot(airbnb['calculated_host_listings_count'], color='#974F6E', ax=axes[1, 1], kde=True, stat="density", linewidth=0)
sns.histplot(airbnb['availability_365'], color='#55335E', ax=axes[1, 2], kde=True, stat="density", linewidth=0)
f.suptitle("Density plots", fontsize = 30)
f.tight_layout()
plt.show()

After visualising distributions with density plots, it can be observed that neither of them resemble normal distribution. All plots are quite similar, with long right tails.

Therefore, the next step contains removing the outliers.

Removing outliers

To remove outliers an IQR (interquartile range) method was used.

The IQR describes the middle 50% of values when ordered from lowest to highest. To find the interquartile range (IQR), first you need to find the median (middle value) of the lower and upper half of the data. These values are quartile 1 (Q1) and quartile 3 (Q3). The IQR is the difference between Q3 and Q1.

nazwa_kol = ['price','minimum_nights','number_of_reviews','reviews_per_month','calculated_host_listings_count','availability_365']
Q1 = airbnb[nazwa_kol].quantile(0.25)
Q3 = airbnb[nazwa_kol].quantile(0.75)
IQR = Q3 - Q1
airbnb = airbnb[~((airbnb[nazwa_kol] < (Q1 - 1.5 * IQR)) |(airbnb[nazwa_kol] > (Q3 + 1.5 * IQR))).any(axis=1)]
    

Additionally, we are going to remove the observations with the price that equals 0.

airbnb= airbnb.drop(airbnb[(airbnb['price']==0)].index)

Once again we are checking the descriptive statistics for the dataset, now after removing the outliers.

airbnb.describe()

	host_id	latitude	longitude	price	minimum_nights	number_of_reviews	reviews_per_month	calculated_host_listings_count	availability_365
count	3.310600e+04	33106.000000	33106.000000	33106.000000	33106.000000	33106.000000	33106.000000	33106.000000	33106.000000
mean	7.938232e+07	48.864573	2.347423	87.854226	151.407298	9.272277	0.278156	1.129614	59.863167
std	1.011875e+08	0.019227	0.034204	46.084590	176.519159	11.899767	0.356374	0.396893	111.194356
min	9.412000e+03	48.812220	2.221440	8.000000	1.000000	0.000000	0.000000	1.000000	0.000000
25%	1.307353e+07	48.849940	2.325320	55.000000	2.000000	1.000000	0.010000	1.000000	0.000000
50%	3.457965e+07	48.866300	2.349900	78.000000	7.000000	4.000000	0.140000	1.000000	0.000000
75%	1.000057e+08	48.880970	2.373980	105.000000	365.000000	14.000000	0.410000	1.000000	69.000000
max	4.263279e+08	48.905690	2.467120	260.000000	730.000000	51.000000	1.890000	3.000000	365.000000

After removing the outliers, the standard deviation decreased in value. A significant difference was observed for the mean price, which dropped from 130 to almost 88 euro. The average number of reviews per apartment is 12, whereas the most popular offers have 51 of them. The mean availability is almost 60 days but there are offers with 0 availability and the full availability (= fully available throughout the entire year).

Categorical data

We're going to find out what neighbourhoods are present in the dataset and with what frequency they occur:

airbnb.neighbourhood.value_counts()

Buttes-Montmartre      4238
Popincourt             3479
Vaugirard              2613
Entrepôt               2504
Batignolles-Monceau    2369
Ménilmontant           2313
Buttes-Chaumont        2279
Passy                  1532
Reuilly                1525
Opéra                  1490
Observatoire           1364
Gobelins               1354
Temple                 1139
Panthéon                945
Bourse                  759
Hôtel-de-Ville          740
Luxembourg              711
Palais-Bourbon          706
Élysée                  595
Louvre                  451
Name: neighbourhood, dtype: int64

The most offers come from Buttes-Montmartre and Popincourt.

Now, we are going to see how the neighbourhoods are distributed on the map of Paris.

plt.figure(figsize=(14,10))
sns.scatterplot(x='longitude', y='latitude', hue='neighbourhood',s=20, data=airbnb, palette='cubehelix')
plt.title("Neighbourhoods' layout", fontsize = 30)
plt.show()

Next, we want to check what types of properties are in the dataset.

airbnb.room_type.value_counts()

Entire home/apt    28135
Private room        4645
Shared room          229
Hotel room            97
Name: room_type, dtype: int64

In the dataset, the rentals of entire home or apartment outbalance the rest. On the second position there are private rooms and later shared rooms and lastly hotel rooms.

Data visualisation

Correlation plot

Firstly, we are going to see the correlation heat plot for all the variables.

plt.figure(figsize=(15,8))
sns.heatmap(airbnb.corr(),annot=True, cmap='flare')
plt.title('Wykres korelacji zmiennych zbioru', fontsize = 20)
plt.show()

From the plot we can see that in the dataset there aren't present any strong correlations. The only higher value are 'reviews_per_month' and 'number_of_reviews' which is justified.

The relationship between the number of reviews and its price (divided into property's types)

Despite the fact that there is no linear correlation between these attributes, we are going to see how these variables look like collated with division into property's type.

sns.set_style('white')
sns.color_palette("flare", as_cmap=True)

g = sns.FacetGrid(airbnb, col='room_type', sharey=False)

g.map_dataframe(sns.scatterplot, x='price', y='number_of_reviews', color="#B8656E")
g.set_axis_labels('Price', 'Number of reviews')
g.set_titles(col_template='{col_name}')
g.add_legend()
g.fig.subplots_adjust(top=0.7)
g.fig.suptitle('Number of reviews and price of the rental property', fontsize = 20);

For the first type of property it is difficult to find any relationship. For private rooms from around 130 euro per night the offers have rather less reviews, not many exceeds 30. In case of shared room, there are many observations with the very low or 0 number of reviews, in majority it pertains to offers below 100 euro. For the hotels, it's evident that the prices are the highest and it can be said that the higher the price the higher number of reviews it has.

The relationship between the availability and its price (divided into property's types)

Similarly, the variables don't show the linear correlation but we are going to see how these variables look like collated with division into property's type.

g = sns.FacetGrid(airbnb, col='room_type', sharey=False)

g.map_dataframe(sns.scatterplot, x='price', y='availability_365', color="#914D6E")
g.set_axis_labels('Price', 'Availability')
g.set_titles(col_template='{col_name}')
g.add_legend()
g.fig.subplots_adjust(top=0.7)
g.fig.suptitle('Availabilty and price of the rental property', fontsize = 20);

In this case, also the first type of property doesn't show the relationship between two variables. In case of private rooms, from the price above around 150 euro, the availability is rather higher but there are still offers with 0 availability. Shared rooms in the medium range of prices show 0 availability whereas for the cheapest the availability is various. On the other hand, hotel rooms can be characterised as the ones with high availability, however there can be found observations with 0 availability as well.

The average prices of rentals depending its type

title="The average prices of rentals depending its type "
result = airbnb.groupby(['room_type'])['price'].aggregate(np.mean).reset_index().sort_values('price')
plt.figure(figsize=(15,8))
sns.barplot(x='room_type', y='price', data=airbnb, order=result['room_type'], palette="flare")
plt.title(title, fontsize = 20)
plt.xlabel('Room type')
plt.ylabel('Price')
plt.ioff()

The prices per night in hotels are the highest, they total 170 euro on average, next there are entire homes/apartments (around 90 euro), private rooms (around 60 euro) and the cheapest ones are shared rooms with the 45 euro per night averagely.

The accurate mean values collated with the average number of reviews and availability are presented below:

sample1 = airbnb.groupby("room_type").agg({
        "price": [ "mean"], 
                "number_of_reviews": ["mean"],
                    "availability_365": ["mean"]})
sample1

	price	number_of_reviews	availability_365
	mean	mean	mean
room_type
Entire home/apt	92.541070	9.576719	59.329838
Hotel room	170.185567	9.701031	260.134021
Private room	59.823681	7.528741	58.861787
Shared room	45.720524	7.052402	60.868996

The average prices of rentals depending neighbourhood they're locatewd in

title="The average price of rental in different neighbourhoods"
result = airbnb.groupby(['neighbourhood'])['price'].aggregate(np.mean).reset_index().sort_values('price')
plt.figure(figsize=(15,8))
sns.barplot(x='price', y='neighbourhood', data=airbnb, order=result['neighbourhood'], palette="flare")
plt.title(title, fontsize = 20)
plt.xlabel('Price')
plt.ylabel('Neighbourhood')
plt.ioff()

The most expensive neighbourhoods for rent are Louvre, Luxembourg and Élysée where the average price per night is 115 euro. On the other hand, the cheapest ones are Gobelins and Buttes-Chaumont where prices oscilate between 70 euro. The neighbourhood where the most offers are located in - Buttes-Montmartre is characterised by relatively low prices, on average slightly below 80 euro per night. It is understandable, as the more listings in the area the bigger the competition, meaning the prices have to be competitive.

The accurate mean values collated with the average number of reviews and availability are presented below:

sample2 = airbnb.groupby("neighbourhood").agg({
        "price": [ "mean"], 
                "number_of_reviews": ["mean"],
                                    "availability_365": ["mean"]})
sample2

	price	number_of_reviews	availability_365
	mean	mean	mean
neighbourhood
Batignolles-Monceau	86.119038	7.704939	62.269312
Bourse	105.090909	11.509881	73.667984
Buttes-Chaumont	72.037297	8.250987	51.039930
Buttes-Montmartre	78.303445	8.784804	50.777017
Entrepôt	87.799521	10.222444	50.011581
Gobelins	76.771049	8.709749	57.299852
Hôtel-de-Ville	112.185135	11.124324	75.013514
Louvre	116.321508	10.534368	75.667406
Luxembourg	114.998594	10.137834	84.696203
Ménilmontant	71.019023	8.821444	50.850843
Observatoire	83.186217	9.563783	67.132698
Opéra	95.775839	9.157047	56.944295
Palais-Bourbon	106.237960	9.288952	66.145892
Panthéon	99.291005	10.489947	64.449735
Passy	105.293734	8.332898	96.372715
Popincourt	83.368784	10.049439	50.423972
Reuilly	80.930492	8.950820	58.501639
Temple	106.285338	11.339772	63.992976
Vaugirard	89.407960	8.926139	57.691160
Élysée	115.857143	8.709244	95.231933

Top 100 offers with the highest number of reviews

top_rev = airbnb.nlargest(100, 'number_of_reviews')

plt.figure(figsize=(15,8))
plt.scatter(x='price', y= 'neighbourhood', c='availability_365', data=top_rev, alpha=0.5, cmap='flare')
plt.colorbar(label = 'Availability')
plt.xlabel('Price')
plt.ylabel('Neighbourhood')
plt.title('Top 100 the most popular offers', fontsize = 20)
plt.grid()
plt.show()

The most popular neighbourhoods, meaning the ones with the highest number of reviews, are Popincourt and Vaugirard. On the other hand, the least popular are Opéra and Palais-Bourbon.

The colours of the points represent the availability of the offers. Mostly, these colours are yellowish and orangish which is indicative of rather low and moderate availabity of these offers. It makes sense as these are the listings that are willingly reviewed hence it is expected for them to be in demand.

However, there exist popular rentals with high availability. It can be explained by the fairly higher prices of these offers (with some exceptions).

Nevertheless the most offers fit in the range 50-150 euro per night. It can be said that the lower the price the bigger the number of rentals with low availability.

The accurate values from above figure are presented below:

top_rev.groupby('neighbourhood').agg({'neighbourhood':["count"], 'price':['mean'], 'availability_365':['mean']})

	neighbourhood	price	availability_365
	count	mean	mean
neighbourhood
Batignolles-Monceau	2	58.500000	0.000000
Bourse	8	102.375000	109.500000
Buttes-Chaumont	8	81.125000	92.125000
Buttes-Montmartre	9	69.111111	70.333333
Entrepôt	8	88.750000	94.125000
Gobelins	2	99.500000	27.000000
Hôtel-de-Ville	3	81.333333	122.333333
Louvre	2	142.500000	80.500000
Luxembourg	3	78.333333	0.000000
Ménilmontant	5	68.600000	115.400000
Observatoire	6	134.833333	127.666667
Opéra	1	70.000000	0.000000
Palais-Bourbon	1	130.000000	1.000000
Panthéon	3	127.333333	47.666667
Passy	4	104.000000	229.750000
Popincourt	15	104.733333	98.533333
Reuilly	3	53.000000	0.000000
Temple	2	108.000000	155.000000
Vaugirard	12	122.000000	87.000000
Élysée	3	130.333333	258.000000

Top 100 offers presented on the map

The exact location of the most popular offers is presented on the map below.

The red icons mark the most popular tourist attractions in Paris. After clicking on the icon the name of the place is shown.

# Create the map

m_2 = folium.Map(location=[48.86, 2.35], tiles='cartodbpositron', zoom_start=12.2)

tooltip = "Click me!"

# Add points to the map
mc = MarkerCluster()
for idx, row in top_rev.iterrows():
    if not math.isnan(row['longitude']) and not math.isnan(row['latitude']):
        mc.add_child(Marker([row['latitude'], row['longitude']]))
m_2.add_child(mc)

folium.Marker(
    location=[48.858093, 2.294694],
    popup="The Eiffel Tower",
    icon=folium.Icon(color="red"),
    tooltip=tooltip
).add_to(m_2)

folium.Marker(
    location=[48.860294, 2.338629],
    popup="Musée du Louvre",
    icon=folium.Icon(color="red"),
    tooltip=tooltip
).add_to(m_2)

folium.Marker(
    location=[48.873756, 2.294946],
    popup="Arc de Triomphe",
    icon=folium.Icon(color="red"),
    tooltip=tooltip
).add_to(m_2)

folium.Marker(
    location=[48.852737, 2.350699],
    popup="Notre Dame",
    icon=folium.Icon(color="red"),
    tooltip=tooltip
).add_to(m_2) 


# Display the map
m_2

Make this Notebook Trusted to load map: File -> Trust Notebook

The most of the top offers is located in some distance from the main tourist attractions in Paris. More rentals (from the most popular group) can be found in the north area of Paris.

The most booked offers presented on the map

most_booked = airbnb[airbnb['availability_365'] < 25]   

# Create a base map
m_1 = folium.Map(location=[48.86, 2.35], tiles='cartodbpositron', zoom_start=12)

# Add a heatmap to the base map
HeatMap(data=most_booked[['latitude', 'longitude']], radius=10).add_to(m_1)

# Display the map
m_1

Make this Notebook Trusted to load map: File -> Trust Notebook

The highest density of the most booked offers (colour yellow and orange) is observed in the north of Paris, with the biggest number of rentals located in the outskirts. The reason for that is the price accessibility of the offers located there.

The accurate values are presented below:

sample3 = most_booked.groupby("neighbourhood").agg({
     "neighbourhood": [ "count"],
           "price": [ "mean"], 
                "number_of_reviews": ["mean"],
                                    "availability_365": ["mean"]})
sample3

	neighbourhood	price	number_of_reviews	availability_365
	count	mean	mean	mean
neighbourhood
Batignolles-Monceau	1687	78.090101	6.953764	0.720213
Bourse	509	96.003929	10.880157	0.726916
Buttes-Chaumont	1713	67.266783	7.464682	0.746060
Buttes-Montmartre	3132	72.870051	8.208174	0.792465
Entrepôt	1866	81.980707	9.333869	0.700965
Gobelins	949	70.678609	7.851423	0.533193
Hôtel-de-Ville	471	102.861996	9.626327	0.953291
Louvre	288	107.177083	10.045139	0.920139
Luxembourg	422	102.867299	8.030806	0.796209
Ménilmontant	1705	66.112610	8.106158	0.700293
Observatoire	938	77.446695	8.196162	0.491471
Opéra	1071	88.211018	8.107376	0.688142
Palais-Bourbon	469	96.272921	8.618337	0.524520
Panthéon	644	91.242236	9.656832	0.818323
Passy	867	94.455594	6.765859	0.754325
Popincourt	2604	76.682028	9.061060	0.753840
Reuilly	1076	75.379182	8.208178	0.624535
Temple	792	98.325758	9.992424	1.055556
Vaugirard	1876	83.797974	7.914712	0.570362
Élysée	335	104.143284	6.776119	0.549254

Visualisation of all the offers on the map of Paris

On the map there are presented all the offers from the dataset, where a particular colour represents the price (in a given scale) and the size of dots marks the number of reviews (the bigger the dot the more reviews the offer has). Moreover, after hovering over the point the more detailed information is displayed such as the exact price, number of reviews, name of the neighbourhood, the property type and its availability.

def location_map(df, color, size, title):
    fig = px.scatter_mapbox(airbnb, lat='latitude', lon='longitude',
                       size = size,
                       color = color,
                       size_max = 8,
                       zoom = 10,
                       height = 500,
                       title = title,
                       hover_data = {'latitude' : False,
                                     'longitude' : False,
                                    'neighbourhood' : True,
                                    'price': True,
                                    'room_type' : True,
                                    'number_of_reviews' : True,
                                    'availability_365' : True})
    fig.update_layout(mapbox_style="open-street-map")
    fig.show()

location_map(airbnb, 'price', 'number_of_reviews', 'Wizualizacja ofert na mapie Paryża')

As it could be expected, the observations located further from the city centre tend to have lower prices. The closer to the city centre the more offers with higher prices per night. The number of reviews hardly depends on localisation, the rentals with a high number of reviews can be found in the outskirts as well as in the city centre.

The advantage of this kind of visualisation is that if we have a certain localisation we're interested in, it is easy to zoom in the map and compare the offers in a given area using the information available after hovering over a particular point.

Conclusions

After conducting the analysis all targets were met.

To conclude:

• In the dataset there aren't any strong correlations.
• The biggest number of offers is located in Buttes-Montmartre where the average price per night is almost 80 euro.
• The cheapest neighbourhoods for rental (with the average price 70 euro) are Ménilmontant and Buttes-Chaumont where the average availability is also the lowest (there are a lot of bookings in this area).
• The bigggest number of offers with the biggest number of reviews is in Popincourt (the average price for the most popular rentals is 104 euro) and Vaugirard (122 euro on average). The availability of the most popular offers is rather low or moderate and most of these offers have the price in between 50-150 euro per night. The lower the price the more offers with low availability.
• The majority of offers pertains to the rental of the whole property (apartment/home). Their average prices per night are around 92 euro. On the other hand, the most expensive are the hotel rooms with the price 170 euro per night but there are not many hotel room's offers in the dataset. The cheapest are shared rooms where the price is 46 euro on average.
• The majority of the most popular offers is located in some distance from the main tourist attractions in Paris. Besides, more rentals (from the most popular group) can be found in the north area of Paris.
• The highest density of the most booked offers is observed in the north of Paris, with the biggest number of rentals located in the outskirts. The reason for that is the price accessibility of the offers located there.
• The closer to the city centre the more offers with higher prices per night. The number of reviews hardly depends on localisation, the rentals with a high number of reviews can be found in the outskirts as well as in the city centre.